Case
A 67-year-old man presents to the hospital with persistent, subjective fevers and malaise for one month, subacute onset of dyspnea, and nonproductive cough for the preceding six days. The patient is a nonsmoker, denies sick contacts, and has had no foreign travel. What would be the best approach to making the diagnosis while working to enhance diagnostic skills?
Diagnostic Reasoning
With clinical experience, making a diagnosis can become so routine that physicians might not contemplate their problem-solving strategies. Diagnostic reasoning is the process of thinking about a clinical problem to form a diagnosis. Experienced clinicians typically rely upon nonanalytic reasoning (i.e., pattern recognition) for straightforward problems, reverting to analytic reasoning if a pattern is not recognized.
The literature describes five steps in the reasoning process (see Figure 1). In the early stages of data collection, hypotheses emerge that feed back into data collection behaviors as the clinician seeks confirmatory evidence. This complex interplay between data collection and hypothesis generation/elimination leads to a more clearly defined understanding of the patient’s presentation. The synthesis of the patient’s presentation, including epidemiologic risk factors, symptoms, signs, and laboratory and radiologic studies, is called the “problem representation.” After a clinician conceives the problem representation, he or she reviews the mental representations of diseases (i.e., illness scripts) to determine hypotheses by finding disease presentations that best match the formulated problem representation (see Figure 2).
Figure 1. Demonstration of the non-linear nature of clinical reasoning highlighting the critical influence of context on data collection, hypothesis generation, and access to illness script knowledge.
Analytic and nonanalytic reasoning. In what is known as the dual process theory, diagnostic reasoning is believed to occur both analytically and nonanalytically.1 Nonanalytic reasoning is often exemplified by rapid, subconscious “pattern recognition” and is developed through clinical experience and other nonclinical learning experiences (e.g. reading).
Conversely, analytic reasoning, the “slow,” conscious, cognitive processing, is typically utilized when a patient presentation is complicated or does not fit a known disease pattern. Clinicians apply both strategies to make diagnoses in evaluating complex cases.
(Click for larger image)Figure 2. The process of diagnostic reasoning
In the outlined case, while the symptoms of fever and cough might lead to the diagnosis of community-acquired pneumonia (CAP), the time course seems unusually long. This atypical pattern for CAP could trigger analytic reasoning, leading to new considerations such as tuberculosis (TB).
Case Continued
On examination, the patient has severe rigors and diaphoresis, as well as a fever of 39.4°C and a heart rate of 102 bpm. Full examination discloses mild end-expiratory wheezes and bronchial breath sounds in the right lower lobe. The remainder of his examination is normal. Labs reveal WBC 8.5×103, hemoglobin 11g/dL, MCV of 92 fL, and platelet count 22,000 mm3. Blood cultures, sputum cultures, and respiratory virus microarray are normal. The chest X-ray (CXR) is unremarkable.
Further history reveals that the patient is a sheepherder living in a primitive earthen structure in the rural mountains of western New Mexico.
Problem representation revisited. With additional historical, laboratory, and radiological data collected, further interpretation and synthesis occur. Salient elements are highlighted and prioritized, irrelevant details are discarded, and data of uncertain relevance are reevaluated as additional data are gathered. The problem representation—an interpreted, subjective mental model of a patient’s clinical presentation—is updated and reformulated. The verbal expression of the problem representation is variously called the assessment, summary statement, or “one-liner.” Within this summary statement, and fundamental to the creation of a strong problem representation, is the incorporation of “semantic qualifiers.”